1. Field of the Invention
The present invention generally relates to the field of computer image processing of computer tomography (CT) images and, more particularly, to the problem of detecting the boundaries of objects appearing in such images.
2. Background Description
U.S. Pat. No. 5,416,815, which is incorporated here by reference in its entirety, describes computer tomography (CT) systems and the method of image reconstruction from projections.
Objects of interest in an X-ray computed tomography (CT) image are typically identified by applying image processing algorithms directly to the CT image. Such algorithms, known as a segmentation algorithms, detect objects of interest by identifying the boundaries of these objects (or equivalently by identifying the pixels belonging to each object).
An alternative method of detecting objects in a CT image is to directly process the two-dimensional (2D) projection data from which the two-dimensional CT image is subsequently reconstructed.
In this alternative approach, sharp discontinuities (edges) in the two-dimensional projection data (which can be considered as a 2D image) are first detected, and then object boundaries in the CT image are identified by making use of the following facts:
each ray generated by the CT scanner produces a single point in the projection image, PA1 each ray generated by the CT scanner that is tangent to a convex object being scanned will appear as an edge in the projection image, and PA1 each point in the projection image corresponds to a line segment in the CT image that is reconstructed from the projection image. PA1 uses projection data, and optionally tomographic image data, to detect the boundaries of objects in tomographic images; PA1 is of particular value for boundary-detection in images corrupted by reconstruction artifacts, such as CT scans of hospital patients who have implanted metal prostheses; PA1 is applicable to recovering the boundaries of convex objects, and the convex segments of the boundaries of concave objects (though not the complete boundaries of the concave objects themselves); and PA1 is clinically viable in the sense that this method can be embodied in a practical, real-world system that can be used routinely in hospitals and medical clinics, and relies only on data that are available from standard medical CT scanners, that is the CT images and scout images (and not the original sinogram data).
This approach, while potentially usef uil, suffers from the fact that in practice projection data are usually noisy. Therefore, there are invariably localization errors in the edge-detection. Even small errors in projection data can lead to large boundary-detection errors in the CT image.
To solve this noisy-data problem, we provide a system and method for resolving the inconsistencies induced by edge-detection errors. This method applies constraints that are imposed by the geometry of the CT scanner and the scanning process. This method is applicable for the detection of the boundaries of convex objects, and the convex sections of the boundaries of concave objects, in CT images.
The prior art in boundary recovery from projection data includes: M. Bergstrom et al., "Determination of Object Contour from Projections for Attenuation Correction in Cranial Positron Emission Tomography", Journal of Computer Assisted Tomography, April 1992, 6(2), pp. 365-372; Jean-Philippe Thirion, "A Geometric Alternative to Computed Tomography", INRIA Research Report No. 1463 (June 1991), INRIA, 78153 Le Chesnay Cedex, France; Jean-Philippe Thirion and Nicholas Ayache, "Method and device for examining a body, particularly for tomography", U.S. Pat. No. 5,421,330, (1995); N. Srinivasa, K. R. Ramakrishnan, and K. Rajgopal, "Detection of edges from projections", IEEE Trans. Medical Imaging, March 1992, 11(1), pp.76-80; Ralph Benjamin, "Method and apparatus for generating images", U.S. Pat. No. 5,647,018 (1997).
Thirion recognized the problem of the inconsistencies that are introduced during edge-detection of the projection data. The proposed solution works by removing inconsistencies in the order that they are detected. As Thirion notes (page 18 of INRIA research report 1463 cited above), while this method has the advantage of being fast, it has the disadvantage that it can produce inaccurate results.